IBM Code Model Asset Exchange: Facial Age Estimator

This repository contains code to instantiate and deploy a facial age estimation model. The model detects faces in an image, extracts facial features for each face detected and finally predicts the age of each face. The model uses a coarse-to-fine strategy to perform multi-class classification and regression for age estimation. The input to the model is an image and the output is a list of estimated ages and bounding box coordinates of each face detected in the image. The format of the bounding box coordinates is [xmin, ymin, width, height].

The model is based on the SSR-Net model. The model files are hosted on IBM Cloud Object Storage. The code in this repository deploys the model as a web service in a Docker container. This repository was developed as part of the IBM Code Model Asset Exchange.

Model Metadata

Domain	Application	Industry	Framework	Training Data	Input Data Format
Vision	Facial Recognition	General	Keras & TensorFlow	IMDB-WIKI Dataset	Image (PNG/JPG)

References:

• T.-Y. Yang, Y.-H. Huang, Y.-Y. Lin, P.-C. Hsiu, and Y.-Y. Chuang. "SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation", IJCAI, 2018.
• SSR-Net Github Repository

Licenses

Component	License	Link
This repository	Apache 2.0	LICENSE
Model Weights	MIT	LICENSE
Model Code (3rd party)	MIT	LICENSE
Test assets	Various	Asset README

Pre-requisites:

• docker: The Docker command-line interface. Follow the installation instructions for your system.
• The minimum recommended resources for this model is 2GB Memory and 1 CPU.

Steps

1. Deploy from Docker Hub
2. Deploy on Kubernetes
3. Run Locally

Deploy from Docker Hub

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 codait/max-facial-age-estimator

This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it. If you'd rather checkout and build the model locally you can follow the run locally steps below.

Deploy on Kubernetes

You can also deploy the model on Kubernetes using the latest docker image on Docker Hub.

On your Kubernetes cluster, run the following commands:

$ kubectl apply -f https://github.com/IBM/MAX-Facial-Age-Estimator/raw/master/max-facial-age-estimator.yaml

The model will be available internally at port 5000, but can also be accessed externally through the NodePort.

Run Locally

1. Build the Model
2. Deploy the Model
3. Use the Model
4. Development
5. Clean Up

1. Build the Model

Clone this repository locally. In a terminal, run the following command:

$ git clone https://github.com/IBM/MAX-Facial-Age-Estimator.git

Change directory into the repository base folder:

$ cd MAX-Facial-Age-Estimator

To build the docker image locally, run:

$ docker build -t max-facial-age-estimator .

Note that currently this docker image is CPU only (we will add support for GPU images later).

2. Deploy the Model

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-facial-age-estimator

3. Use the Model

The API server automatically generates an interactive Swagger documentation page. Go to http://localhost:5000 to load it. From there you can explore the API and also create test requests.

Use the model/predict endpoint to load a test image (you can use one of the test images from the assets folder) and get predictions for the image from the API.

You can also test it on the command line, for example:

$ curl -F "image=@assets/tom_cruise.jpg" -XPOST http://localhost:5000/model/predict

You should see a JSON response like that below:

{
    "status": "ok",
    "predictions": [
        {
            "age_estimation": 48,
            "face_box": [
                303,
                174,
                379,
                515
            ]
        }
    ]
}

4. Development

To run the Flask API app in debug mode, edit config.py to set DEBUG = True under the application settings. You will then need to rebuild the docker image (see step 1).

5. Cleanup

To stop the Docker container, type CTRL + C in your terminal.